RGB-D face recognition under various conditions via 3D constrained local model

Abstract

This research proposes a method for 3D face recognition in various conditions using 3D constrained local model (CLM-Z). In this method, a combination of 2D images (RGBs) and depth images (Ds) captured by Kinect has been used. After detecting the face and smoothing the depth image, CLM-Z model has been used to model and detect the important points of the face. These points are described using Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), and 3D Local Binary Patterns (3DLBP). Finally, each face is recognized by a Support Vector Machine (SVM). The challenging situations are changes of lighting, facial expression and head pose. The results on CurtinFaces and IIIT-D datasets demonstrate that the proposed method outperformed state-of-the-art methods under illumination, expression and pitch pose conditions and comparable results were obtained in other cases. Additionally, our proposed method is robust even when the training data has not been carefully collected.